Intelligent learning systems are systems that attempt to assist students in achieving specific learning goals. To date, these systems have mainly used a computerized teaching approach that minors the approach taken in brick-and-mortar classrooms. Each student is presented with the same lecture, content, and assessment, regardless of learning style, intelligence, or cognitive characteristics.
Advances in intelligent learning systems have been limited to approaches such as “adaptive learning.” These approaches are usually applied to logic-based topics such as mathematics, where the content that is served to each student is based on a pre-determined course-specific decision tree that is hard-coded into the system. If a first student and a second student each fail the same assessment by missing the same questions, both students will be presented with the same remedial materials as dictated by the decision tree.
Online courses are examples of “containers” that may employ adaptive learning technology to achieve a specific goal. For any given container, the adaptive learning technology used by the container is largely self-contained. That is, the adaptive learning technology employed by a container is programmed for a singular unchanging goal associated with the container.
For example, an adaptive learning tool may be designed to teach a student a course on the fundamentals of calculus. The designer of the tool will assume that the student possesses the foundational knowledge of mathematics required to begin the course, but the tool may provide a certain amount of “review” information as a means of calibration. In addition, the tool will not take into consideration the goals of any other course in which the student may be engaged. Instead, the tool will be designed to help the student achieve a particular level of proficiency in calculus. Once that level of efficiency is obtained by the student, the tool becomes useless. While data, such as assessment scores, may be saved, the core logic of the adaptive learning tool provides no additional benefit to the student unless the student decides to re-take the course or a portion of the course.
The illusion of adaptivity in “adaptive learning” tools is achieved by providing a dynamic experience for the student. This experience is based on the relationship between the assessment scores of the student and the pre-programmed hierarchy included in the tool. However, existing tools do not actually “adapt” to the student. Instead, by performing in a particular way, the student merely traverses down a pre-existing path through the tool's hierarchy.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.